The learners build powerful image recognition models efficiently using pre-trained deep learning networks. The course covers the fundamentals of computer vision and convolutional neural networks (CNNs), followed by hands-on training with popular transfer learning architectures like VGG, ResNet, Inception, and MobileNet. Learners will understand how to fine-tune models, optimize performance with limited data, and deploy scalable image classification solutions. By working on real-world datasets and projects, this course bridges the gap between theory and application, enabling students to create accurate, production-ready AI models for domains such as healthcare, retail, security, and autonomous systems.
Module 1: Introduction to AI & Image Classification Basics of AI, ML & Deep Learning Types of Image Classification Problems Image datasets, labels & annotations Overview of Transfer Learning.
Module 2: Convolutional Neural Networks (CNNs) Fundamentals CNN architecture & layers Feature extraction & filters Pooling, activation functions Overfitting & regularization techniques.
Module 3: Transfer Learning Concepts What is Transfer Learning? Feature extraction vs Fine-tuning Popular pre-trained models (VGG, ResNet, Inception, MobileNet, EfficientNet).
Module 4: Data Preparation for Image Classification Dataset collection & preprocessing Image augmentation techniques Splitting train/test/validation sets Data pipeline automation.
Module 5: Working with Pre-trained Models in TensorFlow & Keras Loading and customizing pre-trained models Freezing & unfreezing layers Implementing custom classification heads Hands-on: Build a model using MobileNet.
Module 6: Performance Optimization Hyperparameter tuning (batch size, epochs, LR, optimizer) Dropout & BatchNorm strategies GPU/TPU acceleration options Hands-on: Optimize model accuracy.
Module 7: Evaluation & Visualization Accuracy, Precision, Recall, F1-score Confusion matrix, ROC-AUC Model explainability (Grad-CAM heatmaps).
Module 8: Deployment of Image Classification Models Model export formats (SavedModel, TFLite, ONNX) Deployment on web, mobile & edge devices Hands-on: Deploy a model with Streamlit / Flask.
Module 9: Real-Time Image Classification Live camera input processing Object detection vs classification Integrating models with OpenCV for real-time performance.
Module 10: Capstone Project & Certification Domain-based project options: Healthcare, Retail, Security, Agriculture End-to-end implementation Presentation & Certification evaluation.
Mobile: 9100348679Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
Email: coursedivine@gmail.com
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